Quantization made by Richard Erkhov.
japanese-stablelm-3b-4e1t-instruct - GGUF
- Model creator: https://huggingface.co/stabilityai/
- Original model: https://huggingface.co/stabilityai/japanese-stablelm-3b-4e1t-instruct/
Original model description:
language: - ja tags: - japanese-stablelm - causal-lm pipeline_tag: text-generation license: apache-2.0 extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I allow Stability AI to contact me about information related to its models and research: checkbox
Japanese StableLM-3B-4E1T Instruct
Model Description
This is a 3B-parameter decoder-only Japanese language model fine-tuned on instruction-following datasets, built on top of the base model Japanese StableLM-3B-4E1T Base.
If you are in search of a larger model, please check Japanese Stable LM Instruct Gamma 7B.
Usage
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("stabilityai/japanese-stablelm-3b-4e1t-instruct")
model = AutoModelForCausalLM.from_pretrained(
"stabilityai/japanese-stablelm-3b-4e1t-instruct",
trust_remote_code=True,
torch_dtype="auto",
)
model.eval()
if torch.cuda.is_available():
model = model.to("cuda")
def build_prompt(user_query, inputs="", sep="\n\n### "):
sys_msg = "ไปฅไธใฏใใฟในใฏใ่ชฌๆใใๆ็คบใจใๆ่ใฎใใๅ
ฅๅใฎ็ตใฟๅใใใงใใ่ฆๆฑใ้ฉๅใซๆบใใๅฟ็ญใๆธใใชใใใ"
p = sys_msg
roles = ["ๆ็คบ", "ๅฟ็ญ"]
msgs = [": \n" + user_query, ": \n"]
if inputs:
roles.insert(1, "ๅ
ฅๅ")
msgs.insert(1, ": \n" + inputs)
for role, msg in zip(roles, msgs):
p += sep + role + msg
return p
# Infer with prompt without any additional input
user_inputs = {
"user_query": "ไธใใใใใใจใใใฎๆๅณใๅฐๅญฆ็ใงใๅใใใใใซๆใใฆใใ ใใใ",
"inputs": "ๆ
ใใฏไบบใฎใใใชใใ"
}
prompt = build_prompt(**user_inputs)
input_ids = tokenizer.encode(
prompt,
add_special_tokens=False,
return_tensors="pt"
)
tokens = model.generate(
input_ids.to(device=model.device),
max_new_tokens=256,
temperature=1,
top_p=0.95,
do_sample=True,
)
out = tokenizer.decode(tokens[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
print(out)
Model Details
- Developed by: Stability AI
- Model type:
Japanese StableLM-3B-4E1T Instructmodel is an auto-regressive language model based on the transformer decoder architecture. - Language(s): Japanese
- License: This model is licensed under Apache License, Version 2.0.
- Contact: For questions and comments about the model, please join Stable Community Japan. For future announcements / information about Stability AI models, research, and events, please follow https://twitter.com/StabilityAI_JP.
Model Architecture
The model is a decoder-only transformer similar to the LLaMA (Touvron et al., 2023) architecture with the following modifications:
| Parameters | Hidden Size | Layers | Heads | Sequence Length |
|---|---|---|---|---|
| 2,795,443,200 | 2560 | 32 | 32 | 4096 |
- Position Embeddings: Rotary Position Embeddings (Su et al., 2021) applied to the first 25% of head embedding dimensions for improved throughput following Black et al. (2022).
- Normalization: LayerNorm (Ba et al., 2016) with learned bias terms as opposed to RMSNorm (Zhang & Sennrich, 2019).
- Tokenizer: GPT-NeoX (Black et al., 2022).
Training Datasets
- Japanese translation of the Databricks Dolly-15k dataset
- Japanese translation of the subset of the Anthropic HH dataset
- Wikinews subset of the izumi-lab/llm-japanese-dataset
Use and Limitations
Intended Use
The model is intended to be used by all individuals as a foundational model for application-specific fine-tuning without strict limitations on commercial use.
Limitations and bias
The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model-generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.
Credits
The fine-tuning was carried out by Fujiki Nakamura. Other aspects, including data preparation and evaluation, were handled by the Language Team of Stability AI Japan, notably Meng Lee, Makoto Shing, Paul McCann, Naoki Orii, and Takuya Akiba.
Acknowledgements
We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.
We are also appreciative of AI Novelist/Sta (Bit192, Inc.) and the numerous contributors from Stable Community Japan for assisting us in gathering a large amount of high-quality Japanese textual data for model training.